The RVL-CDIP corpus [1]} is widely used for benchmarking the performance of document classifiers.
This corpus consists of images of scanned documents, and most early efforts on the RVL-CDIP classification task used convolutional neural networks (CNNs), including [2]}, [3]}, [1]}, [5]}, [6]}.
Later work incorporated the textual modality along with visual features (e.g., [7]}, [8]}, [9]}, [10]}).
The recent advent of transformers has seen researchers employ this architecture to the classification task.
This includes models that solely use image features (e.g., [11]}, [12]}, [13]}) as well as models that incorporate page layout and optical character recognition (OCR) features (e.g., [14]}, [15]}, [16]}, [17]}, [18]}, [19]}, [20]}, [21]}, [22]}, [23]}, [24]}, [25]}, [26]}).
Despite the prominence of the RVL-CDIP corpus in evaluating document classifiers, there is no published OOD corpus for benchmarking models on out-of-distribution performance.
